In recent years, the automobile industry attempts to implement an in-vehicle system in an automobile in such a manner that a display of an in-vehicle terminal is installed not only on the front seat (i.e., anterior seat) but also on the rear seat (i.e., backseat). By so doing, images on a DVD (digital versatile disk) or car-navigation system can be viewed on both the front seat and the rear seat via an in-vehicle LAN (local area network). In order to implement such an in-vehicle system, it may be preferable to reduce at least one of the band of image data to be transmitted, the size of a circuit to be realized, and a delay in the transmission. The reduced delay will also be preferable in the future where advanced safety is achieved by transmitting an image captured by an in-vehicle camera.
The standardized compression system currently used is made so as to encode image data by two-dimensional subband coding regarding a high compression rate as most important, and has not been sufficient to reduce a circuit size or delay amount so as to be preferable for in-vehicle installment.
In order to satisfy the conditions related to, for example, the above-mentioned preferable circuit size and reduced delay, reduction in the memory usage or reduction in the size of the unit processed in compression may be important. For this reason, an algorithm that performs the compression process using a memory corresponding to about one line, not the compression process in units of blocks, is preferable. As techniques for performing the compression process using a line memory, following techniques have been known.
A certain technique uses one-dimensional orthogonal transform to (1) obtain the low-order conversion coefficient to quantize the difference between the obtained low-order conversion coefficient and the similar coefficient of the immediately preceding line, followed by variable length coding and to (2) obtain a high-frequency component as the difference between the image corresponding to the quantized low-order conversion coefficient and the original image, followed by quantization and variable length coding of the obtained high-frequency component.
Since the system involves the one-dimensional process only, there is an advantage that the memory amount that significantly influences the LSI (large scale integration) circuit size can be reduced. Furthermore, high compression can be attained as two-dimensional correlation is also used by obtaining the difference between the current line and the immediately preceding line.
In addition, since the quantization and encoding are performed separately for the low-frequency component of (1) and the high-frequency component of (2), the quantizers can be designed in accordance with the respective components, making it possible to effectively reduce visual redundancy. In other words, in view of the characteristics of vision, smoothness of gradation is important for the low-frequency component, and rough resolution can be ignored. On the other hand, the resolution is important for the high-frequency component, and rough gradation can be ignored. The quantization can be performed in accordance with this characteristic of vision.
Wavelet transform coding is described below, while it does not assume a compression process using a memory of one line only. Wavelet transform coding is also used for JPEG (Joint Photographic Experts Group) 2000 instead of the orthogonal transform coding used in the above-explained technique.
Wavelet transform coding is a technique for performing coding after dividing an image for every spatial-frequency band. According to the method, in the image domain, usually, the band division is sequentially performed using low-pass filters to make the band half. In addition, the method is also realized as transform coding in the frequency domain by using basis functions that correspond to the respective bands.
Haar wavelet transform is a simple example of the transformation in the frequency domain in the wavelet transform. FIGS. 1A and 1B illustrate and compares basis functions for an eight-pixel block; those of the orthogonal transform are illustrated in FIG. 1A and those of Haar wavelet transform are illustrated in FIG. 1B.
Wavelet transform uses scaling functions and analyzing functions, and when the functions are added together, the basis functions as illustrated in FIG. 1B are obtained.
The orthogonal transform and the wavelet transform have a difference in characteristics as illustrated in FIGS. 2A and 2B. That is, in the orthogonal transform (see FIG. 2A), the frequency resolution of each coefficient after the transformation is the same across the entire block of pixels, and the spatial resolution of each coefficient after the transformation is the same across the entire block of pixels. On the other hand, in the wavelet transform (see FIG. 2B), the frequency resolution is higher and the spatial resolution is lower for a lower frequency component, and the frequency resolution is lower and the spatial resolution is higher for a higher frequency component. In the wavelet transform, “octave decomposition” of the band is sequentially performed from layer to layer to make the spatial resolution ½ and to double the frequency resolution.
This feature of the wavelet transform matches a characteristic of vision. Human eyes are sensitive to even a small gradation change that represents smoothness in a part in which the gradation change is smooth (namely, in which the gradation change corresponds to a low-frequency component), but become less sensitive to the spatial position. On the contrary, in the edge part of a line image (namely, in a part corresponding to a high-frequency component), human eyes are less sensitive to the gradation change but sensitive to the special position of the edge. According to the wavelet transform coding, the image quality can be adjusted in accordance with the characteristics of vision using this feature to obtain a high compression rate.
Specifically, as illustrated in FIG. 3, a filtering process for the first layer is performed for an original image 31 to separate it into two bands of a high frequency (H1) 32 and a low frequency (L1) 33. Then, in a phased manner, a filtering process for the second layer is performed for the low frequency (L1) 33 to separate it into a high frequency (H2) 34 and a low frequency (L2) 35.
The above-mentioned steps are the same as those in DCT (Discrete Cosine Transform) in that the compression is performed by applying the fact that frequency components of an image generally concentrate at low frequencies. However, the filtering is not performed for the high-frequency component (H1) 32 again in the wavelet transform as illustrated in FIG. 3. For this reason, an advantage that mosquito noises do not easily stick out is obtained because the high-frequency component is not expressed in units of many pixels, e.g., not expressed in units of eight pixels as in JPEG (Joint Photographic Experts Group).
However, since a large number of pixels are used for the filtering process for the low-frequency side, there is a problem that the transformation result does not settle within 8 bits and the compression process using a memory corresponding to one line is difficult. For example, the filters used for the lossless JPEG2000 are as follows.x′i=−0.5xi−1+xi−0.5xi+1  High-frequency filter H(x):x′2i=−(⅛)x2i−2+(¼)x2i−1+(¾)x2i+(¼)x2i+1−(⅛)x2i+2  Low-frequency filter L(x):
Meanwhile, compression using the slant transform is also known. The compression performance of the slant transform (SLT) is regarded as approximately the same as that of the DCT described above, and regarding the computation complexity, the computation complexity can be reduced compared to that of the DCT by using approximate calculation for the division. For example, the SLT matrix for the horizontally-aligned four pixels illustrated in FIG. 4 is as illustrated in FIG. 5. In the SLT matrix for the horizontally-aligned four pixels, the transformation result by the first row represents the flat component and the transformation result by the second row represents the simple slant component. Note that the flat and simple slant components are generally regarded as important components for a natural image.
Meanwhile, simple transformation that adopts the mean value of two pixels as the low-frequency filter and the differential value of two pixels as the high-frequency component exists in techniques such as Haar wavelet transform described above. However, it is known that an attempt to improve the compression rate with such filters causes significant degradation of image quality.
In addition, a technique performing filtering as described below for four pixels is known.rounddown((x0+x1+x2+x3)/4+0.5)rounddown((x0−x1−x2+x3)/2)x0−x3 x1−x2 
where rounddown (x) is a maximum integer that does not exceed x (where x is a real number)
The above technique focuses on the fact that transformation and inverse transformation can be performed for the differential signal (i.e., high-frequency component) and the integerized mean value (i.e., low-frequency component) even if more than two pixels are used to calculate the differential signal and the integerized mean value.
Another technique is also known in which the above filtering is modified to perform filtering capable of pseudo-discrete cosine transform.
In the techniques described above, Haar wavelet transform with which the computation complexity is reduced has a feature that can provide conformity with the characteristics of vision. However, Haar wavelet transform has a problem that the image quality is degraded in a natural image having smooth gradation, because it uses stair-like step functions as basis functions.
In the wavelet transform, in addition to a method for transforming an image into the frequency domain, there is a method for creating images having different resolutions by dividing spatial-frequency band in the pixel domain using low-pass filters. However, the latter method has a problem that the computation complexity and the memory usage are increased. It has yet another problem that coefficients having different degrees of importance cannot be obtained to be utilized for the data amount compression as in the transformation into the frequency domain.
In addition, there is a problem that with a simple filtering or a small filter size (i.e., a small number of pixels used for the calculation of coefficients), it is difficult to obtain the correlation between pixels and improvement of the compression rate is difficult.    Non-patent document 1: ALI HABIBI, “Hybrid Coding of Pictorial Data”, IEEE Trans. on Commun. Vol. COM-22, No. 5, pp. 1075, May. 1974    Non-patent document 2: “From Orthogonal Transform to Wavelet” found at tech.nagaokaut.ac.jp/lecturenote/H14GRAD_PDF/NOTE11.PDF (access confirmation: 20 Mar. 2007)    Non-patent document 3: W. K. Pratt. W-H. Chen, L. R. Welch, “Slant Transform Image Coding”, IEEE Trans. on Commun. Vol. COM-22, No. 8, pp. 1075, August 1974    Patent document 1: Japanese Laid-open Patent Publication No. 04-020115    Non-patent document 4: Komatsu, Kunitoshi and Sezaki, Kaoru. Noutan gazou no kagyakuteki na henkanfugouka [Reversible transform coding of images]. The Transactions of the Institute of Electronics, Information and Communication Engineers. A Vol. J79-A, No. 4, pp. 981-990, April 1996.